Overview

Dataset statistics

Number of variables8
Number of observations1574274
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory96.1 MiB
Average record size in memory64.0 B

Variable types

Numeric8

Dataset

DescriptionThis profiling report was generated for Python Assignment 4
URLhttps://www.assignment4.com/bitcoin/
Copyright(c) Shubham Mishra 2021

Alerts

Timestamp is highly correlated with Open and 5 other fieldsHigh correlation
Open is highly correlated with Timestamp and 5 other fieldsHigh correlation
High is highly correlated with Timestamp and 5 other fieldsHigh correlation
Low is highly correlated with Timestamp and 5 other fieldsHigh correlation
Close is highly correlated with Timestamp and 5 other fieldsHigh correlation
Volume_(BTC) is highly correlated with Volume_(Currency)High correlation
Volume_(Currency) is highly correlated with Timestamp and 6 other fieldsHigh correlation
Weighted_Price is highly correlated with Timestamp and 5 other fieldsHigh correlation
Timestamp is highly correlated with Open and 4 other fieldsHigh correlation
Open is highly correlated with Timestamp and 4 other fieldsHigh correlation
High is highly correlated with Timestamp and 4 other fieldsHigh correlation
Low is highly correlated with Timestamp and 4 other fieldsHigh correlation
Close is highly correlated with Timestamp and 4 other fieldsHigh correlation
Volume_(BTC) is highly correlated with Volume_(Currency)High correlation
Volume_(Currency) is highly correlated with Volume_(BTC)High correlation
Weighted_Price is highly correlated with Timestamp and 4 other fieldsHigh correlation
Timestamp is highly correlated with Open and 4 other fieldsHigh correlation
Open is highly correlated with Timestamp and 4 other fieldsHigh correlation
High is highly correlated with Timestamp and 4 other fieldsHigh correlation
Low is highly correlated with Timestamp and 4 other fieldsHigh correlation
Close is highly correlated with Timestamp and 4 other fieldsHigh correlation
Volume_(BTC) is highly correlated with Volume_(Currency)High correlation
Volume_(Currency) is highly correlated with Volume_(BTC)High correlation
Weighted_Price is highly correlated with Timestamp and 4 other fieldsHigh correlation
Timestamp is highly correlated with Open and 4 other fieldsHigh correlation
Open is highly correlated with Timestamp and 4 other fieldsHigh correlation
High is highly correlated with Timestamp and 4 other fieldsHigh correlation
Low is highly correlated with Timestamp and 4 other fieldsHigh correlation
Close is highly correlated with Timestamp and 4 other fieldsHigh correlation
Volume_(BTC) is highly correlated with Volume_(Currency)High correlation
Volume_(Currency) is highly correlated with Volume_(BTC)High correlation
Weighted_Price is highly correlated with Timestamp and 4 other fieldsHigh correlation
Volume_(Currency) is highly skewed (γ1 = 23.51722215) Skewed
Timestamp has unique values Unique

Reproduction

Analysis started2021-11-15 15:01:13.212300
Analysis finished2021-11-15 15:02:30.057784
Duration1 minute and 16.85 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Timestamp
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct1574274
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1468131457
Minimum1417411980
Maximum1515369600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 MiB
2021-11-15T20:32:30.232777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1417411980
5-th percentile1425636039
Q11444527315
median1468141410
Q31491755505
95-th percentile1510646781
Maximum1515369600
Range97957620
Interquartile range (IQR)47228190

Descriptive statistics

Standard deviation27285002.82
Coefficient of variation (CV)0.01858484994
Kurtosis-1.196379039
Mean1468131457
Median Absolute Deviation (MAD)23614110
Skewness-0.002393726514
Sum2.311241181 × 1015
Variance7.44471379 × 1014
MonotonicityStrictly increasing
2021-11-15T20:32:30.369377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14470348801
 
< 0.1%
14437876201
 
< 0.1%
15081095401
 
< 0.1%
14229639001
 
< 0.1%
14838267601
 
< 0.1%
14664921001
 
< 0.1%
14873943001
 
< 0.1%
14292471601
 
< 0.1%
15043251601
 
< 0.1%
14271745801
 
< 0.1%
Other values (1574264)1574264
> 99.9%
ValueCountFrequency (%)
14174119801
< 0.1%
14174120401
< 0.1%
14174121001
< 0.1%
14174121601
< 0.1%
14174122201
< 0.1%
14174122801
< 0.1%
14174123401
< 0.1%
14174124001
< 0.1%
14174124601
< 0.1%
14174125201
< 0.1%
ValueCountFrequency (%)
15153696001
< 0.1%
15153695401
< 0.1%
15153694801
< 0.1%
15153694201
< 0.1%
15153693601
< 0.1%
15153693001
< 0.1%
15153692401
< 0.1%
15153691801
< 0.1%
15153691201
< 0.1%
15153690601
< 0.1%

Open
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct270983
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1705.117812
Minimum0.06
Maximum19891.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 MiB
2021-11-15T20:32:30.501397image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.06
5-th percentile229.54
Q1290.3
median590.05
Q31224.49
95-th percentile7379.94
Maximum19891.99
Range19891.93
Interquartile range (IQR)934.19

Descriptive statistics

Standard deviation3059.03798
Coefficient of variation (CV)1.794033209
Kurtosis12.66161364
Mean1705.117812
Median Absolute Deviation (MAD)335.18
Skewness3.442331539
Sum2684322639
Variance9357713.364
MonotonicityNot monotonic
2021-11-15T20:32:30.627808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
225.512557
 
0.2%
3782219
 
0.1%
226.322188
 
0.1%
2241515
 
0.1%
3701459
 
0.1%
2601378
 
0.1%
2101095
 
0.1%
1891051
 
0.1%
2161031
 
0.1%
150965
 
0.1%
Other values (270973)1558816
99.0%
ValueCountFrequency (%)
0.061
< 0.1%
109.871
< 0.1%
109.941
< 0.1%
110.131
< 0.1%
110.21
< 0.1%
110.661
< 0.1%
110.821
< 0.1%
111.121
< 0.1%
111.371
< 0.1%
111.461
< 0.1%
ValueCountFrequency (%)
19891.994
< 0.1%
198912
< 0.1%
19890.991
 
< 0.1%
19890.681
 
< 0.1%
19890.54
< 0.1%
198904
< 0.1%
198892
< 0.1%
19888.881
 
< 0.1%
19888.11
 
< 0.1%
198881
 
< 0.1%

High
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct253286
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1706.024854
Minimum0.06
Maximum19891.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 MiB
2021-11-15T20:32:30.768134image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.06
5-th percentile229.6
Q1290.41
median590.21
Q31224.81
95-th percentile7380.64
Maximum19891.99
Range19891.93
Interquartile range (IQR)934.4

Descriptive statistics

Standard deviation3061.434202
Coefficient of variation (CV)1.79448394
Kurtosis12.66613727
Mean1706.024854
Median Absolute Deviation (MAD)335.26
Skewness3.443151746
Sum2685750572
Variance9372379.373
MonotonicityNot monotonic
2021-11-15T20:32:30.901984image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
225.512550
 
0.2%
3782243
 
0.1%
226.322180
 
0.1%
2241674
 
0.1%
3701513
 
0.1%
2601441
 
0.1%
2101190
 
0.1%
1891051
 
0.1%
2161026
 
0.1%
150965
 
0.1%
Other values (253276)1558441
99.0%
ValueCountFrequency (%)
0.061
< 0.1%
109.941
< 0.1%
111.891
< 0.1%
112.531
< 0.1%
115.071
< 0.1%
116.661
< 0.1%
117.311
< 0.1%
117.661
< 0.1%
117.991
< 0.1%
118.521
< 0.1%
ValueCountFrequency (%)
19891.995
< 0.1%
198913
< 0.1%
19890.681
 
< 0.1%
19890.54
< 0.1%
198904
< 0.1%
198892
 
< 0.1%
19888.881
 
< 0.1%
19888.11
 
< 0.1%
198882
 
< 0.1%
19886.121
 
< 0.1%

Low
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct269480
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1704.113168
Minimum0.06
Maximum19891.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 MiB
2021-11-15T20:32:31.046276image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.06
5-th percentile229.48
Q1290.18
median589.98
Q31224.09
95-th percentile7375.8685
Maximum19891.98
Range19891.92
Interquartile range (IQR)933.91

Descriptive statistics

Standard deviation3056.504679
Coefficient of variation (CV)1.793604285
Kurtosis12.65734256
Mean1704.113168
Median Absolute Deviation (MAD)335.17
Skewness3.441538355
Sum2682741053
Variance9342220.853
MonotonicityNot monotonic
2021-11-15T20:32:31.170068image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
225.512552
 
0.2%
3782232
 
0.1%
226.322182
 
0.1%
2241544
 
0.1%
3701473
 
0.1%
2601384
 
0.1%
2101098
 
0.1%
1891051
 
0.1%
2161041
 
0.1%
150965
 
0.1%
Other values (269470)1558752
99.0%
ValueCountFrequency (%)
0.061
< 0.1%
109.871
< 0.1%
109.941
< 0.1%
1101
< 0.1%
110.131
< 0.1%
110.21
< 0.1%
110.481
< 0.1%
110.51
< 0.1%
110.661
< 0.1%
110.671
< 0.1%
ValueCountFrequency (%)
19891.983
< 0.1%
19890.993
< 0.1%
19890.681
 
< 0.1%
19890.494
< 0.1%
19889.994
< 0.1%
19888.992
< 0.1%
19888.881
 
< 0.1%
19888.11
 
< 0.1%
19887.991
 
< 0.1%
19885.111
 
< 0.1%

Close
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct271090
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1705.12341
Minimum0.06
Maximum19891.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 MiB
2021-11-15T20:32:31.313823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.06
5-th percentile229.54
Q1290.3
median590.02
Q31224.49
95-th percentile7379.93
Maximum19891.99
Range19891.93
Interquartile range (IQR)934.19

Descriptive statistics

Standard deviation3059.105067
Coefficient of variation (CV)1.794066663
Kurtosis12.66189191
Mean1705.12341
Median Absolute Deviation (MAD)335.15
Skewness3.442370554
Sum2684331452
Variance9358123.813
MonotonicityNot monotonic
2021-11-15T20:32:31.444528image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
225.512532
 
0.2%
3782230
 
0.1%
226.322180
 
0.1%
2241682
 
0.1%
3701496
 
0.1%
2601415
 
0.1%
2101191
 
0.1%
1891051
 
0.1%
2161036
 
0.1%
150965
 
0.1%
Other values (271080)1558496
99.0%
ValueCountFrequency (%)
0.061
< 0.1%
109.941
< 0.1%
1101
< 0.1%
110.481
< 0.1%
110.51
< 0.1%
110.671
< 0.1%
111.311
< 0.1%
111.471
< 0.1%
111.571
< 0.1%
111.891
< 0.1%
ValueCountFrequency (%)
19891.991
 
< 0.1%
19891.983
< 0.1%
198911
 
< 0.1%
19890.992
< 0.1%
19890.681
 
< 0.1%
19890.54
< 0.1%
198904
< 0.1%
198891
 
< 0.1%
19888.991
 
< 0.1%
19888.881
 
< 0.1%

Volume_(BTC)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct969740
Distinct (%)61.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.073412489
Minimum1 × 10-8
Maximum1563.267113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 MiB
2021-11-15T20:32:31.752669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1 × 10-8
5-th percentile0.0574
Q10.6915
median2.3815
Q37.032457005
95-th percentile27.55502222
Maximum1563.267113
Range1563.267113
Interquartile range (IQR)6.340957005

Descriptive statistics

Standard deviation16.98568743
Coefficient of variation (CV)2.401342698
Kurtosis346.3551446
Mean7.073412489
Median Absolute Deviation (MAD)2.081810245
Skewness12.65412279
Sum11135489.37
Variance288.5135775
MonotonicityNot monotonic
2021-11-15T20:32:31.881125image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0116534
 
1.1%
0.023812
 
0.2%
13374
 
0.2%
0.032532
 
0.2%
0.12300
 
0.1%
102166
 
0.1%
0.051809
 
0.1%
0.041447
 
0.1%
0.061415
 
0.1%
0.21387
 
0.1%
Other values (969730)1537498
97.7%
ValueCountFrequency (%)
1 × 10-827
< 0.1%
2 × 10-83
 
< 0.1%
4 × 10-81
 
< 0.1%
8 × 10-85
 
< 0.1%
9 × 10-81
 
< 0.1%
1 × 10-71
 
< 0.1%
1.1 × 10-71
 
< 0.1%
1.2 × 10-71
 
< 0.1%
1.4 × 10-71
 
< 0.1%
1.6 × 10-74
 
< 0.1%
ValueCountFrequency (%)
1563.2671131
< 0.1%
1156.3194051
< 0.1%
1086.129871
< 0.1%
1068.4472051
< 0.1%
1041.4131421
< 0.1%
931.78410411
< 0.1%
906.045861
< 0.1%
899.83257681
< 0.1%
845.3111
< 0.1%
790.67073221
< 0.1%

Volume_(Currency)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1464368
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22679.27881
Minimum2.6417 × 10-6
Maximum19970764.73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 MiB
2021-11-15T20:32:32.025395image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2.6417 × 10-6
5-th percentile22.35765
Q1316.236084
median1398.623695
Q37601.787068
95-th percentile89921.89698
Maximum19970764.73
Range19970764.73
Interquartile range (IQR)7285.550984

Descriptive statistics

Standard deviation122515.6166
Coefficient of variation (CV)5.402094907
Kurtosis1258.573772
Mean22679.27881
Median Absolute Deviation (MAD)1317.047393
Skewness23.51722215
Sum3.570339897 × 1010
Variance1.50100763 × 1010
MonotonicityNot monotonic
2021-11-15T20:32:32.169671image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.25512304
 
0.1%
2.26322143
 
0.1%
9.82554981359
 
0.1%
0.8961327
 
0.1%
2601224
 
0.1%
1.891051
 
0.1%
2.161024
 
0.1%
3.93958
 
0.1%
4.83902
 
0.1%
3.78746
 
< 0.1%
Other values (1464358)1561236
99.2%
ValueCountFrequency (%)
2.6417 × 10-612
< 0.1%
2.676 × 10-614
< 0.1%
4.2555 × 10-61
 
< 0.1%
4.578 × 10-61
 
< 0.1%
4.8242 × 10-61
 
< 0.1%
4.9064 × 10-61
 
< 0.1%
9.602 × 10-61
 
< 0.1%
2.3896 × 10-51
 
< 0.1%
2.934 × 10-51
 
< 0.1%
3.53768 × 10-52
 
< 0.1%
ValueCountFrequency (%)
19970764.731
< 0.1%
11983480.181
< 0.1%
11250613.261
< 0.1%
9910357.0611
< 0.1%
9615532.161
< 0.1%
9480710.8651
< 0.1%
8777059.0671
< 0.1%
8732203.931
< 0.1%
8529501.6081
< 0.1%
8437343.6441
< 0.1%

Weighted_Price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1265901
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1705.069222
Minimum0.06
Maximum19891.98753
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 MiB
2021-11-15T20:32:32.320594image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.06
5-th percentile229.5457334
Q1290.303099
median590.0206858
Q31224.452841
95-th percentile7379.050129
Maximum19891.98753
Range19891.92753
Interquartile range (IQR)934.149742

Descriptive statistics

Standard deviation3058.975514
Coefficient of variation (CV)1.794047699
Kurtosis12.66178369
Mean1705.069222
Median Absolute Deviation (MAD)335.1498518
Skewness3.442355797
Sum2684246144
Variance9357331.193
MonotonicityNot monotonic
2021-11-15T20:32:32.451684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
225.512451
 
0.2%
3782163
 
0.1%
226.322151
 
0.1%
3701419
 
0.1%
2241399
 
0.1%
2601309
 
0.1%
2101095
 
0.1%
1891051
 
0.1%
2161024
 
0.1%
150965
 
0.1%
Other values (1265891)1559247
99.0%
ValueCountFrequency (%)
0.061
< 0.1%
109.941
< 0.1%
111.3651
< 0.1%
111.891
< 0.1%
114.721
< 0.1%
115.0451
< 0.1%
115.071
< 0.1%
116.5651
< 0.1%
116.661
< 0.1%
116.721
< 0.1%
ValueCountFrequency (%)
19891.987531
< 0.1%
19891.984711
< 0.1%
19891.983291
< 0.1%
19891.272891
< 0.1%
19890.999361
< 0.1%
19890.998231
< 0.1%
19890.886151
< 0.1%
19890.523461
< 0.1%
19890.499731
< 0.1%
19890.498811
< 0.1%

Interactions

2021-11-15T20:32:22.498918image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:31:54.158930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:31:58.715223image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:02.654437image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:06.524733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:10.593143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:14.479117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:18.616344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:22.991061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:31:54.717803image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:31:59.200722image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:03.136017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:07.002772image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:11.079736image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:14.970104image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:19.103049image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:23.477785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:31:55.244618image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:31:59.726333image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:03.595338image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:07.659759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:11.562759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:15.464094image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:19.586327image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:23.963502image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:31:55.769488image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:00.212911image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:04.086680image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:08.131970image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:12.043431image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:15.960765image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:20.067602image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:24.448927image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:31:56.279763image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:00.705127image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:04.569683image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:08.639319image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:12.506321image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:16.465930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:20.552368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:24.947811image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:31:56.791736image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:01.190529image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:05.052641image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:09.123721image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:13.001599image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:16.928393image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:21.034547image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:25.448968image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:31:57.669210image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:01.669784image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:05.539467image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:09.606852image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:13.490009image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:17.598706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:21.502665image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:25.923209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:31:58.191299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:02.153385image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:06.026599image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:10.091823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:13.978521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:18.106046image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-15T20:32:22.006049image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-11-15T20:32:32.568880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-15T20:32:32.733151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-15T20:32:32.881125image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-15T20:32:33.030472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-11-15T20:32:26.166024image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-15T20:32:27.259016image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

TimestampOpenHighLowCloseVolume_(BTC)Volume_(Currency)Weighted_Price
01417411980300.0300.0300.0300.00.013.0300.0
11417412040300.0300.0300.0300.00.013.0300.0
21417412100300.0300.0300.0300.00.013.0300.0
31417412160300.0300.0300.0300.00.013.0300.0
41417412220300.0300.0300.0300.00.013.0300.0
51417412280300.0300.0300.0300.00.013.0300.0
61417412340300.0300.0300.0300.00.013.0300.0
71417412400300.0300.0300.0300.00.013.0300.0
81417412460300.0300.0300.0300.00.013.0300.0
91417412520300.0300.0300.0300.00.013.0300.0

Last rows

TimestampOpenHighLowCloseVolume_(BTC)Volume_(Currency)Weighted_Price
1574264151536906016221.0116221.0116200.6916221.002.36636638368.60593316214.144086
1574265151536912016221.0116221.0116172.2116174.2219.626989317758.57091016189.878976
1574266151536918016174.2216174.2216174.2116174.217.481674121010.22701016174.217319
1574267151536924016174.2216174.2216174.2116174.227.421392120035.17612016174.213396
1574268151536930016174.2116174.2216174.2116174.213.03010349009.54246816174.218650
1574269151536936016174.2116174.2316174.2116174.237.594119122828.95677016174.221301
1574270151536942016174.2316174.2316174.2116174.2211.902468192513.15094016174.221081
1574271151536948016174.2216174.2216174.2116174.213.86084062446.07368416174.218136
1574272151536954016174.2216174.2216174.2116174.221.17909319070.91450916174.219514
1574273151536960016174.2216174.2316174.2216174.225.40122487360.59322216174.220219